To date, the decision paths of artificial intelligence (AI) are so opaque that it is impossible to assess its safety straight away. Artificial intelligence must be transparent for people, however, because only then will it be safe enough to be used in safety-critical systems like autonomous driving.
The enormous potential of autonomous driving cannot be utilized until autonomous vehicles work error-free. After all, in street traffic, human lives are at stake. The reliability of the systems is therefore a particularly pivotal factor. This is where the research work of Fraunhofer IKS comes in.
Cognitive systems receive digital information from sensor data and networks and use it to derive actions. The importance of cognitive systems for a variety of industrial and business sectors will continue to grow because they help us make better decisions. The aim of the Fraunhofer Institute for Cognitive Systems IKS in this process is to combine intelligence and safety.
The enormous increase in system complexity is one of the greatest challenges facing Industry 4.0. Live analyses that run parallel to the machine runtimes to save time and money, as well as predictive maintenance to prevent outages, are part of the diverse methods and tools that the Fraunhofer Institute for Cognitive Systems IKS is developing.
Quantum computing has the potential to effect radical change in many industries, including automotive, chemicals and pharmaceuticals, logistics and the finance and insurance sector. In particular, the increased computing capacities of quantum computing compared to conventional computers make complex simulations and calculations possible, for example.
To meet the high safety demands that result from the increasingly complex electronics in road vehicles and industrial facilities, the Fraunhofer Institute for Cognitive Systems IKS is conducting research in the area of safety engineering.
The goal of smart farming is to make farming more sustainable, more efficient and more resistant. Digitalization and automation can help the agricultural sector to meet challenges like the growing demand for food, the skilled worker shortage and climate-induced extreme weather events. Technologies like machine learning and neural networks make it possible to optimize fertilizer distribution, for example, and enable autonomous driving of agricultural machinery.